Pattern Recognition (6ΕΠ02)
Instructor : Vassilis Plagianakos
Assistant : Georgakopoulos Spyridon
Course typeElective
Semester6
TermSpring Semester
ECTS5
Teaching hours3
Laboratory hours
Description
Pattern recognition systems. Bayesian classifiers, k-nearest neighbor. Parametric estimation of probability density function (maximum Likelihood estimation, maximum a posteriori). Non parametric estimation of probability density function (Parzen windows). Linear classifiers, non linear classifiers. Perceptron algorithm. Multilayer neural networks. Feature generation: contour representation and contour tracing, chain code, polygon, signatures, linear transforms, Fourier Transform, regional features, image recognition, bias and variance, texture.
Textbooks/Bibliography
  • Αναγνώριση Προτύπων, Theodoridis S., BROKEN HILL PUBLISHERS LTD, 1η έκδ./201, ΑΘΗΝΑ
  • Αναγνώριση προτύπων, ΣΤΡΙΝΤΖΗΣ ΜΙΧΑΛΗΣ, ΕΚΔΟΤΙΚΟΣ ΟΙΚΟΣ ΑΔΕΛΦΩΝ ΚΥΡΙΑΚΙΔΗ Α.Ε., 1η/2007, ΘΕΣ/ΝΙΚΗ
Assessment method
Written examination at the end of the semester and optional tasks.